Erik B. Sudderth - Google Scholar

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Alan S. WillskyProfessor of EECS, MITVerified email at mit.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Emily B. FoxUniversity of WashingtonVerified email at uw.edu
William T. FreemanProfessor of Computer Science, MITVerified email at mit.edu
Michael C. HughesAssistant Professor of Computer Science, Tufts UniversityVerified email at michaelchughes.com
Deqing SunResearch Scientist, GoogleVerified email at google.com
Antonio TorralbaProfessor of Computer Science, MITVerified email at csail.mit.edu
Michael J. BlackMax Planck Institute for Intelligent Systems and AmazonVerified email at tuebingen.mpg.de
Zhile RenApplied Research Scientist, AppleVerified email at apple.com
Martin WainwrightProfessor of EECS and Statistics, UC BerkeleyVerified email at berkeley.edu
Michael I MandelAssociate Professor of Computer and Information Science at Brooklyn College, CUNYVerified email at sci.brooklyn.cuny.edu
Stuart RussellProfessor of Computer Science, University of California, BerkeleyVerified email at cs.berkeley.edu
Dae Il KimBrown UniversityVerified email at cs.brown.edu
Alexander IhlerUniversity of California, IrvineVerified email at ics.uci.edu
Soumya GhoshBrown UniversityVerified email at cs.brown.edu
Hanspeter PfisterAn Wang Professor of Computer Science, Harvard UniversityVerified email at seas.harvard.edu
Jyri J. KivinenGE HealthcareVerified email at ge.com
David BleiProfessor of Statistics and Computer Science, Columbia UniversityVerified email at columbia.edu
Finale DoshiAssistant Professor, HarvardVerified email at seas.harvard.edu
Jonas WulffPostdoctoral Researcher, MIT CSAILVerified email at csail.mit.edu

Erik B. Sudderth

Professor of Computer Science, UC Irvine

Verified email at uci.edu - Homepage

Machine Learning Computer Vision Statistics Bayesian Nonparametrics Signal Processing


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Nonparametric belief propagation and facial appearance estimation EB Sudderth, AT Ihler, WT Freeman, AS Willsky	795*	2002
Nonparametric belief propagation EB Sudderth, AT Ihler, WT Freeman, AS Willsky IEEE Conference on Computer Vision & Pattern Recognition, 605-612, 2003	561*	2003
A sticky HDP-HMM with application to speaker diarization EB Fox, EB Sudderth, MI Jordan, AS Willsky The Annals of Applied Statistics, 1020-1056, 2011	451*	2011
Learning hierarchical models of scenes, objects, and parts EB Sudderth, A Torralba, WT Freeman, AS Willsky Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 2 …, 2005	406	2005
An HDP-HMM for systems with state persistence EB Fox, EB Sudderth, MI Jordan, AS Willsky Proceedings of the 25th international conference on Machine learning, 312-319, 2008	308	2008
Distributed occlusion reasoning for tracking with nonparametric belief propagation EB Sudderth, MI Mandel, WT Freeman, AS Willsky Advances in neural information processing systems, 1369-1376, 2005	296	2005
Describing visual scenes using transformed objects and parts EB Sudderth, A Torralba, WT Freeman, AS Willsky International Journal of Computer Vision 77 (1-3), 291-330, 2008	224	2008
Graphical models for visual object recognition and tracking EB Sudderth Massachusetts Institute of Technology, 2006	222	2006
Visual hand tracking using nonparametric belief propagation EB Sudderth, MI Mandel, WT Freeman, AS Willsky 2004 Conference on Computer Vision and Pattern Recognition Workshop, 189-189, 2004	221	2004
Bayesian nonparametric inference of switching dynamic linear models E Fox, EB Sudderth, MI Jordan, AS Willsky IEEE Transactions on Signal Processing 59 (4), 1569-1585, 2011	204	2011
Nonparametric Bayesian learning of switching linear dynamical systems E Fox, EB Sudderth, MI Jordan, AS Willsky Advances in neural information processing systems, 457-464, 2009	203	2009
Shared segmentation of natural scenes using dependent Pitman-Yor processes EB Sudderth, MI Jordan Advances in neural information processing systems, 1585-1592, 2009	199	2009
Describing visual scenes using transformed dirichlet processes A Torralba, AS Willsky, EB Sudderth, WT Freeman Advances in neural information processing systems, 1297-1304, 2006	184	2006
Sharing features among dynamical systems with beta processes E Fox, MI Jordan, EB Sudderth, AS Willsky Advances in neural information processing systems, 549-557, 2009	155	2009
Layered image motion with explicit occlusions, temporal consistency, and depth ordering D Sun, EB Sudderth, MJ Black Advances in Neural Information Processing Systems, 2226-2234, 2010	130	2010
Layered segmentation and optical flow estimation over time D Sun, EB Sudderth, MJ Black 2012 IEEE Conference on Computer Vision and Pattern Recognition, 1768-1775, 2012	124	2012
Efficient multiscale sampling from products of Gaussian mixtures AT Ihler, EB Sudderth, WT Freeman, AS Willsky Advances in Neural Information Processing Systems, 1-8, 2004	101	2004
Embedded trees: Estimation of Gaussian processes on graphs with cycles EB Sudderth, MJ Wainwright, AS Willsky IEEE Transactions on Signal Processing 52 (11), 3136-3150, 2004	99	2004
Learning multiscale representations of natural scenes using Dirichlet processes JJ Kivinen, EB Sudderth, MI Jordan 2007 IEEE 11th International Conference on Computer Vision, 1-8, 2007	96	2007
Truly nonparametric online variational inference for hierarchical Dirichlet processes M Bryant, EB Sudderth Advances in Neural Information Processing Systems, 2699-2707, 2012	93	2012

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